Search Results (734)

We present a psycholinguistic test battery designed to examine the cognitive and affective processes involved in reading Polish poetry. This toolkit combines reader profiling (vocabulary, memory and reading proficiency) with tasks that assess the influence of lexical, textual, affective and poetic features on recognition, context restoration and association generation. Pilot data confirmed the reliability of the measures and their sensitivity to recognised psycholinguistic effects. Vocabulary size and delayed memory rehearsal strongly predicted performance in content restoration, while recognition and association latencies were closely related, indicating shared retrieval mechanisms. Structural and affective properties also influenced responses: line-final words improved recognition but impeded association, with these effects being moderated by word length and frequency. Words that were negatively valenced, abstract and hardly imaginable were restored more accurately than positive or concrete ones. These findings demonstrate the potential of the battery for profiling readers and provide new insights into how Polish poetic language engages memory and associative processes. Full article

In high-concurrency distributed systems, log data often exhibits sequence uncertainty and redundancy, which pose significant challenges to the accuracy and efficiency of anomaly detection. To address these issues, we propose BiGRMT, a hybrid architecture that integrates Bidirectional Gated Recurrent Unit (Bi-GRU) with a Recurrent Memory Transformer (RMT). BiGRMT enhances local temporal feature extraction through bidirectional modeling and adaptive noise filtering using Bi-GRU, while a RMT component is incorporated to significantly extend the model’s capacity for long-sequence modeling via segment-level memory. The Transformer’s multi-head attention mechanism continues to capture global time dependencies but now with improved efficiency due to the RMT’s memory-sharing design. Extensive experiments on three benchmark datasets from LogHub (Spark, BGL(Blue Gene/L), and HDFS (Hadoop distributed file system)) demonstrate that BiGRMT achieves strong results in terms of precision, recall, and F1-score. It attains a precision of 0.913, outperforming LogGPT (0.487) and slightly exceeding Temporal logical attention network (TLAN) (0.912). Compared to LogPal, which prioritizes detection accuracy, BiGRMT strikes a better balance by significantly reducing computational overhead while maintaining high detection performance. Even under challenging conditions such as a 50% increase in log generation rate or 20% injected noise, BiGRMT maintains F1-scores of 87.4% and 83.6%, respectively, showcasing excellent robustness. These findings confirm that BiGRMT is a scalable and practical solution for automated fault detection and intelligent maintenance in complex distributed software systems. Full article

This article analyzes the construction of the Digital Extended Self of digital influencers from the travel channel Travel Channel, drawing on R. W. Belk’s theory. The study employs a qualitative exploratory–descriptive approach, using a case study as its methodological strategy. Data collection involved analyzing five podcast interviews with the channel’s founders, along with videos published between 2022 and 2024. In addition, viewer comments on these videos were extracted and examined. All materials were analyzed using Bardin’s content analysis. The results reveal a strong presence of the Extended Self dimensions, co-construction, and sharing, showing that interaction with the audience actively shapes the influencers’ identity and content. The dimensions of dematerialization (e.g., cloud storage) and distributed memory (the use of digital records as extensions of memory) were also evident. Reincarnation (the use of avatars or personas) was the least observed dimension, a finding attributed to the influencers’ authentic style and focus on real-life experiences. Overall, the Digital Extended Self of the Travel Channel emerges as a genuine and organically constructed entity, resulting in an aggregated Self that reflects a strong connection with its audience. This research provides valuable insights into how Belk’s theory can be applied to the in-depth analysis of digital materials. Full article

As an emerging sports tourism event, Guizhou’s “Village Super League” injects new vitality into the optimization of human–land relationships and the development of household livelihoods in traditional villages of ethnic mountainous regions. Studying five affected traditional tourism villages from an “event–actor–capital” perspective using mixed methods, this research finds the following: (1) The composite average score of household livelihood capital is 0.3177, indicating a medium–low level, which suggests that households’ livelihood structure still requires significant enhancement despite the tourism boost from the “Village Super League”. (2) There is an imbalance in development among the villages. The livelihoods of households under the influence of the “Village Super League” exhibit distinct characteristics, being “driven by external flows, led by social capital, supported by the material foundation, and coordinated with other forms of capital.” (3) The evolution of household livelihoods follows a pathway of “event-driven supplementation, endogenous renewal of actors, capital integration and synergy.” By constructing shared event memory markers, the livelihoods of villages at different stages of tourism development demonstrate differentiated dynamic mechanisms. The findings deepen the theoretical understanding of livelihoods in traditional villages under event-driven development. Consequently, this study recommends that policymakers and community stewards channel transient social capital and external flows into durable physical and financial assets to ensure livelihood sustainability beyond the initial event boom. Full article

This study aims to implement universal logic gates using polarity control within a single silicon transistor structure. For this purpose, a reconfigurable transistor based on a p-i-n structure featuring two polarity gates (PGs) and one control gate was proposed, and its electrical characteristics and logic-in-memory (LIM) circuit operations were analyzed via two-dimensional technology computer-aided design simulations. The proposed device could be perfectly reconfigured into p-channel or n-channel modes because virtual doping effects could be induced according to the polarity of the PG voltage. Moreover, based on the positive feedback and latch-up phenomena, a steep subthreshold swing of approximately 1 mV/dec and a high ON/OFF current ratio of the order of 10¹⁰ were achieved. Building on these characteristics, we successfully verified NAND LIM operation in the p-channel mode and NOR LIM operation in the n-channel mode by connecting two of the proposed devices in parallel. The reconfigurable silicon transistor proposed in this study could perform both NAND and NOR LIM operations while sharing the same device structure and can be expected to play a key role in implementing high-density, low-power LIM systems in the future. Full article

State estimation plays a vital role in UAV navigation and control. With the continuous decrease in sensor cost and size, UAVs equipped with multiple LiDARs, Inertial Measurement Units (IMUs), and cameras have attracted increasing attention. Such systems can acquire rich environmental and motion information from multiple perspectives, thereby enabling more precise navigation and mapping in complex environments. However, efficiently utilizing multi-sensor data for state estimation remains challenging. There is a complex coupling relationship between IMUs’ bias and UAV state. To address these challenges, this paper proposes an efficient and accurate UAV state estimation method tailored for multi-LiDAR–IMU–camera systems. Specifically, we first construct an efficient distributed state estimation model. It decomposes the multi-LiDAR–IMU–camera system into a series of single LiDAR–IMU–camera subsystems, reformulating the complex coupling problem as an efficient distributed state estimation problem. Then, we derive an accurate feedback function to constrain and optimize the UAV state using estimated subsystem states, thus enhancing overall estimation accuracy. Based on this model, we design an efficient distributed state estimation algorithm with multi-LiDAR-IMU-Camerafusion, termed DLIC. DLIC achieves robust multi-sensor data fusion via shared feature maps, effectively improving both estimation robustness and accuracy. In addition, we design an accelerated image-to-point cloud registration module (A-I2P) to provide reliable visual measurements, further boosting state estimation efficiency. Extensive experiments are conducted on 18 real-world indoor and outdoor scenarios from the public NTU VIRAL dataset. The results demonstrate that DLIC consistently outperforms existing multi-sensor methods across key evaluation metrics, including RMSE, MAE, SD, and SSE. More importantly, our method runs in real time on a resource-constrained embedded device equipped with only an 8-core CPU, while maintaining low memory consumption. Full article

Urolithin A (UA), a metabolite of dietary ellagitannins produced by the gut microbiome, is a potential dual-purpose bioactive compound that may interfere with the shared pathogenic pathways linking colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM). This review summarizes recent preclinical and clinical data on UA’s mechanisms, therapeutic potential, and translational challenges. In CRC models, UA promotes G2/M cell cycle arrest, triggers both intrinsic and extrinsic caspase-mediated apoptosis, enhances CD8+ T-cell mitophagy and memory functions, suppresses Wnt/β-catenin signaling, and reduces chemoresistance, especially to 5-FU. For T2DM, UA enhances autophagic flux, mitophagy, insulin signaling, and GLUT4-mediated glucose uptake through the AMPK and PI3K/AKT pathways, reduces fasting glucose and insulin resistance in animal studies, and promotes adipose tissue browning and mitochondrial beta-oxidation. Human biomarker research is limited but indicates positive changes following interventions that increase UA. Future priorities include biomarker-driven, dose-finding trials stratified by metabotype, developing colon-targeted vs. systemic formulations, and testing combinations with chemotherapy and immunotherapy to determine safety and effectiveness. Full article

Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present agriclima, a federated, cloud-native, FAIR-by-design platform that unifies heterogeneous agricultural and environmental datasets under consistent identity, policy, and metadata governance. Its scalable open-source architecture, compliance with INSPIRE and RNDT standards, and privacy-preserving access enable researchers and decision-makers to perform comprehensive analyses with minimal coding, accelerating data-driven agricultural risk management. Developed and tested in a research project by a consortium of stakeholders in agricultural risk management, the platform was evaluated via: (1) FAIR assessment of 26 datasets using F-UJI, (2) system performance monitoring on Kubernetes, and (3) a demonstrative spatiotemporal aggregation use case. It achieved 80% average FAIR compliance, with perfect accessibility (7.00/7.00), while findability and reusability remain key areas for improvement. Performance showed stable operation (CPU 17.24%, memory 49.89%) with capacity headroom. The demonstrative use case validated that researchers can conduct spatiotemporal analyses with minimal coding effort through the abstracted data access components. Beyond technical evaluation, we share lessons learned to guide future platform development and metadata standardization, highlighting the platform’s effectiveness as a foundation for data-driven agricultural decision-making. Full article

In the global trend towards a sustainable circular economy, incineration technology is widely used for the treatment of municipal solid waste (MSW), as it effectively achieves waste harmlessness, reduction, and energy recovery. During the MSW incineration (MSWI) process, the furnace temperature (FT) is closely linked to pollutant emission concentrations. Therefore, precise control and stable monitoring of the FT are essential for minimizing pollution emissions. However, existing studies generally treat the optimization of FT setpoint value and tracking control as separate issues, lacking a unified optimization framework that can link environmental objectives with control parameters in an online, automatic, and closed-loop manner. To address these issues, a dual-layer optimization control method for FT setting and tracking, aimed at minimizing pollutant concentrations, is proposed. In the first layer, the optimization targets the lowest possible NOx and CO₂ emission concentrations, using a genetic algorithm (GA) to determine optimal FT setpoints. In the second layer, the optimization minimizes the Integral of Time-weighted Absolute Error (ITAE) as the performance index, optimizing the parameters of multi-loop PID controllers via an improved GA. Additionally, an innovative shared-memory judgment mechanism is proposed to transmit process data in real time. Based on residual dynamic correction of the optimization function, an effective double-loop closed control architecture is established. Experimental validation shows that, compared to traditional methods, the optimized control system exhibits faster setpoint value tracking, smaller steady-state errors, and stronger anti-interference capabilities, leading to a significant reduction in pollutant emissions. This study provides a new approach for intelligent optimization control in MSWI with substantial application prospects. Full article

Lamport’s Bakery algorithm (LBA) represents a general and elegant solution to the mutual exclusion (ME) problem posed by Dijkstra in 1965. Its correctness is usually based on intuitive reasoning. LBA rests on an unbounded number of tickets, which prevents correctness assessment by model checking. Several variants are proposed in the literature to bound the number of exploited tickets. This paper is based on a formal method centered on Uppaal for reasoning about general shared-memory ME algorithms. A model can (hopefully) be verified by the exhaustive model checker (MC), and/or by the statistical model checker (SMC) through stochastic simulations. To overcome the scalability problems of SMC, a model can be reduced to actors and simulated in Java. The paper formalizes LBA and demonstrates, through simulations, that it is correct with atomic and non-atomic memory registers. Then, some representative variants with bounded tickets are studied, which prove to be accurate with atomic registers, or which confirm their correctness under atomic or non-atomic registers. Full article

Network pharmacology is a powerful approach for studying complex drug–target interactions and biological pathways. However, existing tools often require extensive manual intervention and lack integrated analysis capabilities. Here, we present NeXus v1.2, an automated platform for network pharmacology and multi-method enrichment analysis including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) that addresses these limitations. NeXus v1.2 enables the seamless integration of multi-layer biological relationships, handling complex interactions between genes, compounds, and plants while maintaining analytical rigor. The platform implements three enrichment methodologies: Over-Representation Analysis (ORA), GSEA, and GSVA, circumventing limitations associated with arbitrary threshold-based approaches. NeXus v1.2 was validated using multiple datasets spanning 111 to 10,847 genes, demonstrating robust scalability and performance across dataset sizes. The platform was initially tested using a representative dataset comprising 111 genes, 32 compounds, and 3 plants, showing consistent performance in processing various relationship patterns, including shared compounds between plants and multitargeted genes. The processing time for this dataset was 4.8 s with peak memory usage of 480 MB. Large-scale validation with datasets up to 10,847 genes confirmed scalability, with linear time complexity and completion times under 3 min. NeXus v1.2 automatically generates comprehensive visualizations, including network maps, enrichment analyses, and relationship patterns, while maintaining the biological context of interactions. The tool successfully processed and analyzed enrichment patterns across multiple functional domains, generating publication-quality visualization outputs at 300 DPI resolution. The platform demonstrated enhanced automation in handling incomplete relationship data and maintaining analytical integrity across different biological layers. Compared to manual workflows requiring 15–25 min, NeXus v1.2 reduced the analysis time to under 5 s (>95% reduction) while ensuring the comprehensive coverage of biological relationships. NeXus v1.2 provides improved automation and integration for network pharmacology analysis, offering an efficient and user-friendly platform for complex biological network analysis. Its modular architecture enables the future integration of AI technologies and expansion into various therapeutic applications. Full article

An experiment was conducted to examine whether knowledge of word meanings enables learners to infer the meanings of related words, and whether such transfer is based on memory for related exemplars or for abstract knowledge. Participants completed a word root learning task in which they learned definitions of several English words derived from a shared root (e.g., ambler, noctambulant). At an immediate test, they were assessed on definitions of studied words, new unstudied derivatives (e.g., ambulate), and word roots (e.g., ambul). A multiple regression analysis showed that accuracy on word roots, but not on studied words, predicted performance on new derivatives. These results suggest that transfer of learning was based primarily on more abstract knowledge of word root meanings rather than on memory for specific words. These findings provide novel evidence that learners can apply root-based knowledge to new word forms, and are consistent with theories proposing that transfer is supported by abstract representations. Full article

Mild cognitive impairment (MCI) and sarcopenia are prevalent age-related conditions that often coexist and share common mechanisms such as chronic inflammation, reduced neuroplasticity, and impaired muscle function. Resistance exercise training (RET) has emerged as a promising non-pharmacological strategy capable of addressing both physical and cognitive decline. The aim of this narrative review is to synthesize preclinical and clinical evidence on the effects of RET in older adults with MCI and sarcopenia, with a specific focus on its impact on neuroinflammation, cognitive performance and structural brain changes. At the molecular level, RET activates anabolic pathways, including PI3K/Akt/mTOR, enhances neurotrophic support via BDNF, NT-3, and IGF-1, and promotes hippocampal neurogenesis through exercise-induced myokines such as irisin and cathepsin B. RET also exerts immunomodulatory actions by shifting microglia toward anti-inflammatory M2 phenotypes, attenuating reactive astrogliosis, and supporting oligodendrocyte precursor cell differentiation, thereby improving myelin integrity. Neuroimaging studies consistently report preservation of hippocampal and precuneus gray matter, as well as improved white matter connectivity following RET. Clinically, RET has demonstrated significant and sustained improvements in executive function, memory, and global cognition, with effects persisting for up to 18 months. Collectively, RET represents a multifaceted intervention with the potential to delay progression from MCI to Alzheimer’s disease by integrating neuroprotective, anti-inflammatory, and anabolic effects. Standardization of RET protocols and identification of biomarkers of responsiveness are needed to optimize its role within multimodal dementia-prevention strategies. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

Foot gesture recognition using a continuous-wave (CW) radar requires implementation on edge hardware with strict latency and memory budgets. Existing structured and unstructured pruning pipelines rely on iterative training–pruning–retraining cycles, increasing search costs and making them significantly time-consuming. We propose a NAS-guided bisection hybrid pruning framework on foot gesture recognition from a continuous-wave (CW) radar, which employs a weighted shared supernet encompassing both block and channel options. The method consists of three major steps. In the bisection-guided NAS structured pruning stage, the algorithm identifies the minimum number of retained blocks—or equivalently, the maximum achievable sparsity—that satisfies the target accuracy under specified FLOPs and latency constraints. Next, during the hybrid compression phase, a global L1 percentile-based unstructured pruning and channel repacking are applied to further reduce memory usage. Finally, in the low-cost decision protocol stage, each pruning decision is evaluated using short fine-tuning (1–3 epochs) and partial validation (10–30% of dataset) to avoid repeated full retraining. We further provide a unified theory for hybrid pruning—formulating a resource-aware objective, a logit-perturbation invariance bound for unstructured pruning/INT8/repacking, a Hoeffding-based bisection decision margin, and a compression (code-length) generalization bound—explaining when the compressed models match baseline accuracy while meeting edge budgets. Radar return signals are processed with a short-time Fourier transform (STFT) to generate unique time–frequency spectrograms for each gesture (kick, swing, slide, tap). The proposed pruning method achieves 20–57% reductions in floating-point operations (FLOPs) and approximately 86% reductions in parameters, while preserving equivalent recognition accuracy. Experimental results demonstrate that the pruned model maintains high gesture recognition performance with substantially lower computational cost, making it suitable for real-time deployment on edge devices. Full article